Objectives

This project will develop a ML enhanced security framework for Kademlia-based P2P networks, focusing on detecting and mitigating multiple attack types. The approach consists of:

1. A structured P2P overlay network based on Kademlia will be built using NetworkX in Python. The network will support key-value storage, routing, and peer discovery, ensuring a realistic testing environment.

2. I will introduce different attacks: DDoS flooding, Sybil attacks, routing table poisoning, and eclipse attacks. Each attack will be carefully implemented to assess the network’s vulnerabilities.

3. I will train different ML models, and choose the best one for identifying malicious behaviors. The model will analyze traffic flow, node interactions, and routing anomalies to detect attacks effectively.

4. Using reinforcement learning, the system will dynamically respond to attacks by applying mitigation techniques such as node isolation, traffic throttling, and collaborative defense mechanisms.

5. The framework will be evaluated based on detection accuracy, false positive rate, network latency impact, and mitigation effectiveness to ensure it meets practical security requirements.